Overview

Dataset statistics

 DF1DF2
Number of variables2929
Number of observations70002000
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory1.5 MiB428.2 KiB
Average record size in memory219.1 B219.2 B

Variable types

 DF1DF2
Numeric1919
Categorical1010

Alerts

DF1DF2
balance_indicator is highly imbalanced (54.1%) balance_indicator is highly imbalanced (53.1%) Imbalance
balance_salary_ratio is highly skewed (γ1 = 79.44816936) Alert not present in this datasetSkewed
credit_salary_ratio is highly skewed (γ1 = 76.20886866) credit_salary_ratio is highly skewed (γ1 = 20.90980362) Skewed
estimated_salary has unique values estimated_salary has unique values Unique
customer_value_normalized has unique values customer_value_normalized has unique values Unique
credit_salary_ratio has unique values credit_salary_ratio has unique values Unique
tenure has 297 (4.2%) zeros tenure has 78 (3.9%) zeros Zeros
balance has 2503 (35.8%) zeros balance has 726 (36.3%) zeros Zeros
balance_sqrt has 2503 (35.8%) zeros balance_sqrt has 726 (36.3%) zeros Zeros
age_balance has 2503 (35.8%) zeros age_balance has 726 (36.3%) zeros Zeros
balance_salary_ratio has 2503 (35.8%) zeros balance_salary_ratio has 726 (36.3%) zeros Zeros
tenure_age_ratio has 297 (4.2%) zeros tenure_age_ratio has 78 (3.9%) zeros Zeros

Reproduction

 DF1DF2
Analysis started2024-04-29 17:05:21.5151202024-04-29 17:05:38.952029
Analysis finished2024-04-29 17:05:38.9502512024-04-29 17:05:55.827054
Duration17.44 seconds16.88 seconds
Software versionydata-profiling vv4.7.0ydata-profiling vv4.7.0
Download configurationconfig.jsonconfig.json

Variables

credit_score
Real number (ℝ)

 DF1DF2
Distinct450407
Distinct (%)6.4%20.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean651.23957649.634
 DF1DF2
Minimum350350
Maximum850850
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:55.895401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum350350
5-th percentile489489
Q1584582
median652650
Q3718718
95-th percentile812813
Maximum850850
Range500500
Interquartile range (IQR)134136

Descriptive statistics

 DF1DF2
Standard deviation96.68665696.899467
Coefficient of variation (CV)0.148465570.14916009
Kurtosis-0.42999216-0.45064277
Mean651.23957649.634
Median Absolute Deviation (MAD)6768
Skewness-0.086413127-0.040033766
Sum45586771299268
Variance9348.30949389.5068
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:55.974387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 159
 
2.3%
678 47
 
0.7%
705 39
 
0.6%
710 37
 
0.5%
667 37
 
0.5%
652 37
 
0.5%
645 36
 
0.5%
648 36
 
0.5%
637 35
 
0.5%
640 35
 
0.5%
Other values (440) 6502
92.9%
ValueCountFrequency (%)
850 50
 
2.5%
655 16
 
0.8%
633 16
 
0.8%
683 15
 
0.8%
651 14
 
0.7%
554 14
 
0.7%
670 14
 
0.7%
678 13
 
0.7%
663 12
 
0.6%
660 12
 
0.6%
Other values (397) 1824
91.2%
ValueCountFrequency (%)
350 3
< 0.1%
358 1
 
< 0.1%
359 1
 
< 0.1%
363 1
 
< 0.1%
365 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
376 2
< 0.1%
401 1
 
< 0.1%
404 1
 
< 0.1%
ValueCountFrequency (%)
350 1
 
0.1%
351 1
 
0.1%
386 1
 
0.1%
395 1
 
0.1%
407 1
 
0.1%
411 1
 
0.1%
415 1
 
0.1%
416 3
0.1%
420 1
 
0.1%
422 1
 
0.1%
ValueCountFrequency (%)
350 1
 
< 0.1%
351 1
 
< 0.1%
386 1
 
< 0.1%
395 1
 
< 0.1%
407 1
 
< 0.1%
411 1
 
< 0.1%
415 1
 
< 0.1%
416 3
< 0.1%
420 1
 
< 0.1%
422 1
 
< 0.1%
ValueCountFrequency (%)
350 3
0.1%
358 1
 
0.1%
359 1
 
0.1%
363 1
 
0.1%
365 1
 
0.1%
367 1
 
0.1%
373 1
 
0.1%
376 2
0.1%
401 1
 
0.1%
404 1
 
0.1%

geography
Categorical

 DF1DF2
Distinct33
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
France
3500 
Germany
1755 
Spain
1745 
France
1021 
Germany
504 
Spain
475 

Length

 DF1DF2
Max length77
Median length6.56
Mean length6.00142866.0145
Min length55

Characters and Unicode

 DF1DF2
Total characters4201012029
Distinct characters1212
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st rowGermanyFrance
2nd rowGermanyGermany
3rd rowGermanySpain
4th rowGermanySpain
5th rowGermanySpain

Common Values

ValueCountFrequency (%)
France 3500
50.0%
Germany 1755
25.1%
Spain 1745
24.9%
ValueCountFrequency (%)
France 1021
51.0%
Germany 504
25.2%
Spain 475
23.8%

Length

2024-04-29T14:05:56.106333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:56.161988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:56.206144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
france 3500
50.0%
germany 1755
25.1%
spain 1745
24.9%
ValueCountFrequency (%)
france 1021
51.0%
germany 504
25.2%
spain 475
23.8%

Most occurring characters

ValueCountFrequency (%)
a 7000
16.7%
n 7000
16.7%
r 5255
12.5%
e 5255
12.5%
F 3500
8.3%
c 3500
8.3%
G 1755
 
4.2%
m 1755
 
4.2%
y 1755
 
4.2%
S 1745
 
4.2%
Other values (2) 3490
8.3%
ValueCountFrequency (%)
a 2000
16.6%
n 2000
16.6%
r 1525
12.7%
e 1525
12.7%
F 1021
8.5%
c 1021
8.5%
G 504
 
4.2%
m 504
 
4.2%
y 504
 
4.2%
S 475
 
3.9%
Other values (2) 950
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42010
100.0%
ValueCountFrequency (%)
(unknown) 12029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7000
16.7%
n 7000
16.7%
r 5255
12.5%
e 5255
12.5%
F 3500
8.3%
c 3500
8.3%
G 1755
 
4.2%
m 1755
 
4.2%
y 1755
 
4.2%
S 1745
 
4.2%
Other values (2) 3490
8.3%
ValueCountFrequency (%)
a 2000
16.6%
n 2000
16.6%
r 1525
12.7%
e 1525
12.7%
F 1021
8.5%
c 1021
8.5%
G 504
 
4.2%
m 504
 
4.2%
y 504
 
4.2%
S 475
 
3.9%
Other values (2) 950
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42010
100.0%
ValueCountFrequency (%)
(unknown) 12029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7000
16.7%
n 7000
16.7%
r 5255
12.5%
e 5255
12.5%
F 3500
8.3%
c 3500
8.3%
G 1755
 
4.2%
m 1755
 
4.2%
y 1755
 
4.2%
S 1745
 
4.2%
Other values (2) 3490
8.3%
ValueCountFrequency (%)
a 2000
16.6%
n 2000
16.6%
r 1525
12.7%
e 1525
12.7%
F 1021
8.5%
c 1021
8.5%
G 504
 
4.2%
m 504
 
4.2%
y 504
 
4.2%
S 475
 
3.9%
Other values (2) 950
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42010
100.0%
ValueCountFrequency (%)
(unknown) 12029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7000
16.7%
n 7000
16.7%
r 5255
12.5%
e 5255
12.5%
F 3500
8.3%
c 3500
8.3%
G 1755
 
4.2%
m 1755
 
4.2%
y 1755
 
4.2%
S 1745
 
4.2%
Other values (2) 3490
8.3%
ValueCountFrequency (%)
a 2000
16.6%
n 2000
16.6%
r 1525
12.7%
e 1525
12.7%
F 1021
8.5%
c 1021
8.5%
G 504
 
4.2%
m 504
 
4.2%
y 504
 
4.2%
S 475
 
3.9%
Other values (2) 950
7.9%

gender
Categorical

 DF1DF2
Distinct22
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
Male
3850 
Female
3150 
Male
1070 
Female
930 

Length

 DF1DF2
Max length66
Median length44
Mean length4.94.93
Min length44

Characters and Unicode

 DF1DF2
Total characters343009860
Distinct characters66
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st rowFemaleMale
2nd rowMaleMale
3rd rowFemaleFemale
4th rowMaleMale
5th rowFemaleFemale

Common Values

ValueCountFrequency (%)
Male 3850
55.0%
Female 3150
45.0%
ValueCountFrequency (%)
Male 1070
53.5%
Female 930
46.5%

Length

2024-04-29T14:05:56.257506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:56.302573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:56.340625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 3850
55.0%
female 3150
45.0%
ValueCountFrequency (%)
male 1070
53.5%
female 930
46.5%

Most occurring characters

ValueCountFrequency (%)
e 10150
29.6%
a 7000
20.4%
l 7000
20.4%
M 3850
 
11.2%
F 3150
 
9.2%
m 3150
 
9.2%
ValueCountFrequency (%)
e 2930
29.7%
a 2000
20.3%
l 2000
20.3%
M 1070
 
10.9%
F 930
 
9.4%
m 930
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34300
100.0%
ValueCountFrequency (%)
(unknown) 9860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10150
29.6%
a 7000
20.4%
l 7000
20.4%
M 3850
 
11.2%
F 3150
 
9.2%
m 3150
 
9.2%
ValueCountFrequency (%)
e 2930
29.7%
a 2000
20.3%
l 2000
20.3%
M 1070
 
10.9%
F 930
 
9.4%
m 930
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34300
100.0%
ValueCountFrequency (%)
(unknown) 9860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10150
29.6%
a 7000
20.4%
l 7000
20.4%
M 3850
 
11.2%
F 3150
 
9.2%
m 3150
 
9.2%
ValueCountFrequency (%)
e 2930
29.7%
a 2000
20.3%
l 2000
20.3%
M 1070
 
10.9%
F 930
 
9.4%
m 930
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34300
100.0%
ValueCountFrequency (%)
(unknown) 9860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10150
29.6%
a 7000
20.4%
l 7000
20.4%
M 3850
 
11.2%
F 3150
 
9.2%
m 3150
 
9.2%
ValueCountFrequency (%)
e 2930
29.7%
a 2000
20.3%
l 2000
20.3%
M 1070
 
10.9%
F 930
 
9.4%
m 930
 
9.4%

age
Real number (ℝ)

 DF1DF2
Distinct6962
Distinct (%)1.0%3.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean39.03338.8225
 DF1DF2
Minimum1818
Maximum9285
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:56.401904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum1818
5-th percentile2525
Q13232
median3737
Q34444
95-th percentile6059.05
Maximum9285
Range7467
Interquartile range (IQR)1212

Descriptive statistics

 DF1DF2
Standard deviation10.53790710.332055
Coefficient of variation (CV)0.269974310.26613576
Kurtosis1.47228481.1127748
Mean39.03338.8225
Median Absolute Deviation (MAD)66
Skewness1.04105610.90743236
Sum27323177645
Variance111.04749106.75137
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:56.488728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 342
 
4.9%
38 340
 
4.9%
35 337
 
4.8%
40 321
 
4.6%
36 319
 
4.6%
34 314
 
4.5%
33 305
 
4.4%
39 300
 
4.3%
31 289
 
4.1%
32 277
 
4.0%
Other values (59) 3856
55.1%
ValueCountFrequency (%)
36 102
 
5.1%
38 99
 
5.0%
32 93
 
4.7%
37 92
 
4.6%
33 91
 
4.5%
34 88
 
4.4%
35 80
 
4.0%
40 76
 
3.8%
39 74
 
3.7%
31 73
 
3.6%
Other values (52) 1132
56.6%
ValueCountFrequency (%)
18 15
 
0.2%
19 17
 
0.2%
20 27
 
0.4%
21 33
 
0.5%
22 53
 
0.8%
23 67
1.0%
24 88
1.3%
25 107
1.5%
26 138
2.0%
27 151
2.2%
ValueCountFrequency (%)
18 5
 
0.2%
19 7
 
0.4%
20 7
 
0.4%
21 12
 
0.6%
22 20
1.0%
23 20
1.0%
24 28
1.4%
25 30
1.5%
26 42
2.1%
27 45
2.2%
ValueCountFrequency (%)
18 5
 
0.1%
19 7
 
0.1%
20 7
 
0.1%
21 12
 
0.2%
22 20
0.3%
23 20
0.3%
24 28
0.4%
25 30
0.4%
26 42
0.6%
27 45
0.6%
ValueCountFrequency (%)
18 15
 
0.8%
19 17
 
0.9%
20 27
 
1.4%
21 33
 
1.7%
22 53
 
2.6%
23 67
3.4%
24 88
4.4%
25 107
5.3%
26 138
6.9%
27 151
7.5%

tenure
Real number (ℝ)

 DF1DF2
Distinct1111
Distinct (%)0.2%0.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean5.02957144.9975
 DF1DF2
Minimum00
Maximum1010
Zeros29778
Zeros (%)4.2%3.9%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:56.549293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum00
5-th percentile11
Q132
median55
Q387
95-th percentile910
Maximum1010
Range1010
Interquartile range (IQR)55

Descriptive statistics

 DF1DF2
Standard deviation2.9069212.8743042
Coefficient of variation (CV)0.577965950.57514841
Kurtosis-1.1768023-1.1343309
Mean5.02957144.9975
Median Absolute Deviation (MAD)32
Skewness0.00670256710.0020004045
Sum352079995
Variance8.45018988.2616246
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:56.598563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 739
10.6%
8 723
10.3%
9 716
10.2%
1 711
10.2%
7 711
10.2%
3 705
10.1%
5 704
10.1%
4 691
9.9%
6 654
9.3%
10 349
5.0%
ValueCountFrequency (%)
1 226
11.3%
6 220
11.0%
5 212
10.6%
7 209
10.4%
2 201
10.1%
8 201
10.1%
4 193
9.7%
3 183
9.2%
9 175
8.8%
10 102
5.1%
ValueCountFrequency (%)
0 297
4.2%
1 711
10.2%
2 739
10.6%
3 705
10.1%
4 691
9.9%
5 704
10.1%
6 654
9.3%
7 711
10.2%
8 723
10.3%
9 716
10.2%
ValueCountFrequency (%)
0 78
 
3.9%
1 226
11.3%
2 201
10.1%
3 183
9.2%
4 193
9.7%
5 212
10.6%
6 220
11.0%
7 209
10.4%
8 201
10.1%
9 175
8.8%
ValueCountFrequency (%)
0 78
 
1.1%
1 226
3.2%
2 201
2.9%
3 183
2.6%
4 193
2.8%
5 212
3.0%
6 220
3.1%
7 209
3.0%
8 201
2.9%
9 175
2.5%
ValueCountFrequency (%)
0 297
14.8%
1 711
35.5%
2 739
37.0%
3 705
35.2%
4 691
34.5%
5 704
35.2%
6 654
32.7%
7 711
35.5%
8 723
36.1%
9 716
35.8%

balance
Real number (ℝ)

 DF1DF2
Distinct44961275
Distinct (%)64.2%63.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean76766.16476904.616
 DF1DF2
Minimum00
Maximum238387.56250898.09
Zeros2503726
Zeros (%)35.8%36.3%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:56.666402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum00
5-th percentile00
Q100
median97421.9497431.12
Q3127762.89127884.34
95-th percentile162280.33163763.95
Maximum238387.56250898.09
Range238387.56250898.09
Interquartile range (IQR)127762.89127884.34

Descriptive statistics

 DF1DF2
Standard deviation62175.00262791.27
Coefficient of variation (CV)0.809927170.81648246
Kurtosis-1.4844437-1.4796893
Mean76766.16476904.616
Median Absolute Deviation (MAD)45979.97547413.805
Skewness-0.15346302-0.1391239
Sum5.3736315 × 1081.5380923 × 108
Variance3.8657309 × 1093.9427436 × 109
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:56.743905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2503
35.8%
130170.82 2
 
< 0.1%
105473.74 2
 
< 0.1%
152926.6 1
 
< 0.1%
148654.84 1
 
< 0.1%
157498.9 1
 
< 0.1%
120481.69 1
 
< 0.1%
140720.93 1
 
< 0.1%
157228.61 1
 
< 0.1%
147751.75 1
 
< 0.1%
Other values (4486) 4486
64.1%
ValueCountFrequency (%)
0 726
36.3%
109472.47 1
 
0.1%
62391.22 1
 
0.1%
128472.8 1
 
0.1%
97092.87 1
 
0.1%
127523.75 1
 
0.1%
116099.82 1
 
0.1%
153166.17 1
 
0.1%
120782.7 1
 
0.1%
119852.01 1
 
0.1%
Other values (1265) 1265
63.2%
ValueCountFrequency (%)
0 2503
35.8%
3768.69 1
 
< 0.1%
12459.19 1
 
< 0.1%
14262.8 1
 
< 0.1%
16893.59 1
 
< 0.1%
23503.31 1
 
< 0.1%
24043.45 1
 
< 0.1%
27288.43 1
 
< 0.1%
27517.15 1
 
< 0.1%
27755.97 1
 
< 0.1%
ValueCountFrequency (%)
0 726
36.3%
37702.79 1
 
0.1%
38848.19 1
 
0.1%
40915.55 1
 
0.1%
44582.07 1
 
0.1%
45144.43 1
 
0.1%
45752.78 1
 
0.1%
46323.57 1
 
0.1%
46520.69 1
 
0.1%
47020.65 1
 
0.1%
ValueCountFrequency (%)
0 726
10.4%
37702.79 1
 
< 0.1%
38848.19 1
 
< 0.1%
40915.55 1
 
< 0.1%
44582.07 1
 
< 0.1%
45144.43 1
 
< 0.1%
45752.78 1
 
< 0.1%
46323.57 1
 
< 0.1%
46520.69 1
 
< 0.1%
47020.65 1
 
< 0.1%
ValueCountFrequency (%)
0 2503
125.2%
3768.69 1
 
0.1%
12459.19 1
 
0.1%
14262.8 1
 
0.1%
16893.59 1
 
0.1%
23503.31 1
 
0.1%
24043.45 1
 
0.1%
27288.43 1
 
0.1%
27517.15 1
 
0.1%
27755.97 1
 
0.1%

num_of_products
Categorical

 DF1DF2
Distinct44
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
1
3577 
2
3197 
3
 
182
4
 
44
1
1013 
2
918 
3
 
59
4
 
10

Length

 DF1DF2
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 DF1DF2
Total characters70002000
Distinct characters44
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st row22
2nd row22
3rd row22
4th row12
5th row11

Common Values

ValueCountFrequency (%)
1 3577
51.1%
2 3197
45.7%
3 182
 
2.6%
4 44
 
0.6%
ValueCountFrequency (%)
1 1013
50.6%
2 918
45.9%
3 59
 
2.9%
4 10
 
0.5%

Length

2024-04-29T14:05:56.803204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:56.846092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:56.894002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 3577
51.1%
2 3197
45.7%
3 182
 
2.6%
4 44
 
0.6%
ValueCountFrequency (%)
1 1013
50.6%
2 918
45.9%
3 59
 
2.9%
4 10
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 3577
51.1%
2 3197
45.7%
3 182
 
2.6%
4 44
 
0.6%
ValueCountFrequency (%)
1 1013
50.6%
2 918
45.9%
3 59
 
2.9%
4 10
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3577
51.1%
2 3197
45.7%
3 182
 
2.6%
4 44
 
0.6%
ValueCountFrequency (%)
1 1013
50.6%
2 918
45.9%
3 59
 
2.9%
4 10
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3577
51.1%
2 3197
45.7%
3 182
 
2.6%
4 44
 
0.6%
ValueCountFrequency (%)
1 1013
50.6%
2 918
45.9%
3 59
 
2.9%
4 10
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3577
51.1%
2 3197
45.7%
3 182
 
2.6%
4 44
 
0.6%
ValueCountFrequency (%)
1 1013
50.6%
2 918
45.9%
3 59
 
2.9%
4 10
 
0.5%

has_cr_card
Categorical

 DF1DF2
Distinct22
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
1
4978 
0
2022 
1
1385 
0
615 

Length

 DF1DF2
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 DF1DF2
Total characters70002000
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st row01
2nd row00
3rd row10
4th row00
5th row11

Common Values

ValueCountFrequency (%)
1 4978
71.1%
0 2022
28.9%
ValueCountFrequency (%)
1 1385
69.2%
0 615
30.8%

Length

2024-04-29T14:05:56.954739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:56.997167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:57.035306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 4978
71.1%
0 2022
28.9%
ValueCountFrequency (%)
1 1385
69.2%
0 615
30.8%

Most occurring characters

ValueCountFrequency (%)
1 4978
71.1%
0 2022
28.9%
ValueCountFrequency (%)
1 1385
69.2%
0 615
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4978
71.1%
0 2022
28.9%
ValueCountFrequency (%)
1 1385
69.2%
0 615
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4978
71.1%
0 2022
28.9%
ValueCountFrequency (%)
1 1385
69.2%
0 615
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4978
71.1%
0 2022
28.9%
ValueCountFrequency (%)
1 1385
69.2%
0 615
30.8%

is_active_member
Categorical

 DF1DF2
Distinct22
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
1
3616 
0
3384 
1
1032 
0
968 

Length

 DF1DF2
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 DF1DF2
Total characters70002000
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st row10
2nd row10
3rd row01
4th row10
5th row11

Common Values

ValueCountFrequency (%)
1 3616
51.7%
0 3384
48.3%
ValueCountFrequency (%)
1 1032
51.6%
0 968
48.4%

Length

2024-04-29T14:05:57.080629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:57.121206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:57.158939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 3616
51.7%
0 3384
48.3%
ValueCountFrequency (%)
1 1032
51.6%
0 968
48.4%

Most occurring characters

ValueCountFrequency (%)
1 3616
51.7%
0 3384
48.3%
ValueCountFrequency (%)
1 1032
51.6%
0 968
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3616
51.7%
0 3384
48.3%
ValueCountFrequency (%)
1 1032
51.6%
0 968
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3616
51.7%
0 3384
48.3%
ValueCountFrequency (%)
1 1032
51.6%
0 968
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7000
100.0%
ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3616
51.7%
0 3384
48.3%
ValueCountFrequency (%)
1 1032
51.6%
0 968
48.4%

estimated_salary
Real number (ℝ)

 DF1DF2
Distinct70002000
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean99760.893101527.95
 DF1DF2
Minimum11.5896.27
Maximum199992.48199929.17
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:57.223414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum11.5896.27
5-th percentile9903.36859629.446
Q150895.3152042.318
median99653.25101927.99
Q3148312.11152230.29
95-th percentile189729.65190298.64
Maximum199992.48199929.17
Range199980.9199832.9
Interquartile range (IQR)97416.798100187.97

Descriptive statistics

 DF1DF2
Standard deviation57251.80158198.98
Coefficient of variation (CV)0.573890230.5732311
Kurtosis-1.175156-1.2073805
Mean99760.893101527.95
Median Absolute Deviation (MAD)48749.9750029.66
Skewness0.0027084494-0.019385085
Sum6.9832625 × 1082.0305591 × 108
Variance3.2777688 × 1093.3871213 × 109
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:57.305471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116548.02 1
 
< 0.1%
117034.32 1
 
< 0.1%
155485.24 1
 
< 0.1%
28805.09 1
 
< 0.1%
82276.62 1
 
< 0.1%
50046.25 1
 
< 0.1%
94875.03 1
 
< 0.1%
36221.18 1
 
< 0.1%
152108.47 1
 
< 0.1%
124382.9 1
 
< 0.1%
Other values (6990) 6990
99.9%
ValueCountFrequency (%)
94283.09 1
 
0.1%
27619.06 1
 
0.1%
192434.11 1
 
0.1%
186476.91 1
 
0.1%
81313.51 1
 
0.1%
74771.22 1
 
0.1%
191417.42 1
 
0.1%
148087.62 1
 
0.1%
93261.69 1
 
0.1%
63940.68 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
11.58 1
< 0.1%
90.07 1
< 0.1%
91.75 1
< 0.1%
106.67 1
< 0.1%
123.07 1
< 0.1%
143.34 1
< 0.1%
178.19 1
< 0.1%
247.36 1
< 0.1%
343.38 1
< 0.1%
371.05 1
< 0.1%
ValueCountFrequency (%)
96.27 1
0.1%
142.81 1
0.1%
216.27 1
0.1%
236.45 1
0.1%
287.99 1
0.1%
332.81 1
0.1%
428.23 1
0.1%
502.7 1
0.1%
600.36 1
0.1%
667.66 1
0.1%
ValueCountFrequency (%)
96.27 1
< 0.1%
142.81 1
< 0.1%
216.27 1
< 0.1%
236.45 1
< 0.1%
287.99 1
< 0.1%
332.81 1
< 0.1%
428.23 1
< 0.1%
502.7 1
< 0.1%
600.36 1
< 0.1%
667.66 1
< 0.1%
ValueCountFrequency (%)
11.58 1
0.1%
90.07 1
0.1%
91.75 1
0.1%
106.67 1
0.1%
123.07 1
0.1%
143.34 1
0.1%
178.19 1
0.1%
247.36 1
0.1%
343.38 1
0.1%
371.05 1
0.1%

age_squared
Real number (ℝ)

 DF1DF2
Distinct6962
Distinct (%)1.0%3.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1634.60671613.8845
 DF1DF2
Minimum324324
Maximum84647225
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:57.385292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum324324
5-th percentile625625
Q110241024
median13691369
Q319361936
95-th percentile36003486.95
Maximum84647225
Range81406901
Interquartile range (IQR)912912

Descriptive statistics

 DF1DF2
Standard deviation953.78172913.36444
Coefficient of variation (CV)0.583493090.56594164
Kurtosis5.02500234.1440543
Mean1634.60671613.8845
Median Absolute Deviation (MAD)408408
Skewness1.92948821.756241
Sum114422473227769
Variance909699.57834234.59
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:57.533831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1369 342
 
4.9%
1444 340
 
4.9%
1225 337
 
4.8%
1600 321
 
4.6%
1296 319
 
4.6%
1156 314
 
4.5%
1089 305
 
4.4%
1521 300
 
4.3%
961 289
 
4.1%
1024 277
 
4.0%
Other values (59) 3856
55.1%
ValueCountFrequency (%)
1296 102
 
5.1%
1444 99
 
5.0%
1024 93
 
4.7%
1369 92
 
4.6%
1089 91
 
4.5%
1156 88
 
4.4%
1225 80
 
4.0%
1600 76
 
3.8%
1521 74
 
3.7%
961 73
 
3.6%
Other values (52) 1132
56.6%
ValueCountFrequency (%)
324 15
 
0.2%
361 17
 
0.2%
400 27
 
0.4%
441 33
 
0.5%
484 53
 
0.8%
529 67
1.0%
576 88
1.3%
625 107
1.5%
676 138
2.0%
729 151
2.2%
ValueCountFrequency (%)
324 5
 
0.2%
361 7
 
0.4%
400 7
 
0.4%
441 12
 
0.6%
484 20
1.0%
529 20
1.0%
576 28
1.4%
625 30
1.5%
676 42
2.1%
729 45
2.2%
ValueCountFrequency (%)
324 5
 
0.1%
361 7
 
0.1%
400 7
 
0.1%
441 12
 
0.2%
484 20
0.3%
529 20
0.3%
576 28
0.4%
625 30
0.4%
676 42
0.6%
729 45
0.6%
ValueCountFrequency (%)
324 15
 
0.8%
361 17
 
0.9%
400 27
 
1.4%
441 33
 
1.7%
484 53
 
2.6%
529 67
3.4%
576 88
4.4%
625 107
5.3%
676 138
6.9%
729 151
7.5%

balance_sqrt
Real number (ℝ)

 DF1DF2
Distinct44961275
Distinct (%)64.2%63.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean220.15025219.5452
 DF1DF2
Minimum00
Maximum488.24949500.89728
Zeros2503726
Zeros (%)35.8%36.3%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:57.613492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum00
5-th percentile00
Q100
median312.12488312.13958
Q3357.43935357.6092
95-th percentile402.84033404.67758
Maximum488.24949500.89728
Range488.24949500.89728
Interquartile range (IQR)357.43935357.6092

Descriptive statistics

 DF1DF2
Standard deviation168.23814169.46646
Coefficient of variation (CV)0.764196910.77189785
Kurtosis-1.6278862-1.647571
Mean220.15025219.5452
Median Absolute Deviation (MAD)68.24416669.781781
Skewness-0.45794452-0.44164774
Sum1541051.8439090.39
Variance28304.07328718.883
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:57.691897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2503
35.8%
360.7919345 2
 
< 0.1%
324.7672089 2
 
< 0.1%
391.0583077 1
 
< 0.1%
385.5578296 1
 
< 0.1%
396.8613108 1
 
< 0.1%
347.1047248 1
 
< 0.1%
375.1278849 1
 
< 0.1%
396.52063 1
 
< 0.1%
384.3848982 1
 
< 0.1%
Other values (4486) 4486
64.1%
ValueCountFrequency (%)
0 726
36.3%
330.8662419 1
 
0.1%
249.7823453 1
 
0.1%
358.4310254 1
 
0.1%
311.5972882 1
 
0.1%
357.1046765 1
 
0.1%
340.7342366 1
 
0.1%
391.3644976 1
 
0.1%
347.5380555 1
 
0.1%
346.1964905 1
 
0.1%
Other values (1265) 1265
63.2%
ValueCountFrequency (%)
0 2503
35.8%
61.38965711 1
 
< 0.1%
111.6207418 1
 
< 0.1%
119.4269651 1
 
< 0.1%
129.9753438 1
 
< 0.1%
153.3078928 1
 
< 0.1%
155.0595047 1
 
< 0.1%
165.1921003 1
 
< 0.1%
165.8829407 1
 
< 0.1%
166.6012305 1
 
< 0.1%
ValueCountFrequency (%)
0 726
36.3%
194.1720629 1
 
0.1%
197.0994419 1
 
0.1%
202.2759254 1
 
0.1%
211.144666 1
 
0.1%
212.4721864 1
 
0.1%
213.8989949 1
 
0.1%
215.2291105 1
 
0.1%
215.686555 1
 
0.1%
216.8424543 1
 
0.1%
ValueCountFrequency (%)
0 726
10.4%
194.1720629 1
 
< 0.1%
197.0994419 1
 
< 0.1%
202.2759254 1
 
< 0.1%
211.144666 1
 
< 0.1%
212.4721864 1
 
< 0.1%
213.8989949 1
 
< 0.1%
215.2291105 1
 
< 0.1%
215.686555 1
 
< 0.1%
216.8424543 1
 
< 0.1%
ValueCountFrequency (%)
0 2503
125.2%
61.38965711 1
 
0.1%
111.6207418 1
 
0.1%
119.4269651 1
 
0.1%
129.9753438 1
 
0.1%
153.3078928 1
 
0.1%
155.0595047 1
 
0.1%
165.1921003 1
 
0.1%
165.8829407 1
 
0.1%
166.6012305 1
 
0.1%

log_credit_score
Real number (ℝ)

 DF1DF2
Distinct450407
Distinct (%)6.4%20.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean6.4674216.4649044
 DF1DF2
Minimum5.85793325.8579332
Maximum6.74523636.7452363
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:57.766680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum5.85793325.8579332
5-th percentile6.19236256.1923625
Q16.3699016.3664704
median6.48004466.4769724
Q36.57646966.5764696
95-th percentile6.69950036.7007311
Maximum6.74523636.7452363
Range0.88730320.8873032
Interquartile range (IQR)0.206568590.20999912

Descriptive statistics

 DF1DF2
Standard deviation0.153218950.15337975
Coefficient of variation (CV)0.0236908890.023724983
Kurtosis-0.020372932-0.077132742
Mean6.4674216.4649044
Median Absolute Deviation (MAD)0.101980580.10365967
Skewness-0.46675066-0.41722623
Sum45271.94712929.809
Variance0.0234760470.023525347
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:57.838878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.745236349 159
 
2.3%
6.519147288 47
 
0.7%
6.558197803 39
 
0.6%
6.56526497 37
 
0.5%
6.502790046 37
 
0.5%
6.480044562 37
 
0.5%
6.469250317 36
 
0.5%
6.473890696 36
 
0.5%
6.456769656 35
 
0.5%
6.461468176 35
 
0.5%
Other values (440) 6502
92.9%
ValueCountFrequency (%)
6.745236349 50
 
2.5%
6.484635236 16
 
0.8%
6.450470422 16
 
0.8%
6.52649486 15
 
0.8%
6.478509642 14
 
0.7%
6.317164687 14
 
0.7%
6.507277712 14
 
0.7%
6.519147288 13
 
0.7%
6.49677499 12
 
0.6%
6.492239835 12
 
0.6%
Other values (397) 1824
91.2%
ValueCountFrequency (%)
5.857933154 3
< 0.1%
5.880532986 1
 
< 0.1%
5.883322388 1
 
< 0.1%
5.894402834 1
 
< 0.1%
5.899897354 1
 
< 0.1%
5.905361848 1
 
< 0.1%
5.92157842 1
 
< 0.1%
5.929589143 2
< 0.1%
5.993961427 1
 
< 0.1%
6.001414878 1
 
< 0.1%
ValueCountFrequency (%)
5.857933154 1
 
0.1%
5.860786223 1
 
0.1%
5.955837369 1
 
0.1%
5.978885765 1
 
0.1%
6.008813185 1
 
0.1%
6.018593214 1
 
0.1%
6.02827852 1
 
0.1%
6.03068526 3
0.1%
6.040254711 1
 
0.1%
6.045005314 1
 
0.1%
ValueCountFrequency (%)
5.857933154 1
 
< 0.1%
5.860786223 1
 
< 0.1%
5.955837369 1
 
< 0.1%
5.978885765 1
 
< 0.1%
6.008813185 1
 
< 0.1%
6.018593214 1
 
< 0.1%
6.02827852 1
 
< 0.1%
6.03068526 3
< 0.1%
6.040254711 1
 
< 0.1%
6.045005314 1
 
< 0.1%
ValueCountFrequency (%)
5.857933154 3
0.1%
5.880532986 1
 
0.1%
5.883322388 1
 
0.1%
5.894402834 1
 
0.1%
5.899897354 1
 
0.1%
5.905361848 1
 
0.1%
5.92157842 1
 
0.1%
5.929589143 2
0.1%
5.993961427 1
 
0.1%
6.001414878 1
 
0.1%
 DF1DF2
Distinct1017761
Distinct (%)14.5%38.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean995.03243997.491
 DF1DF2
Minimum350350
Maximum34003388
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:57.910026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum350350
5-th percentile520521
Q1650648
median850850
Q313161320
95-th percentile16221662.1
Maximum34003388
Range30503038
Interquartile range (IQR)666672

Descriptive statistics

 DF1DF2
Standard deviation410.78181415.66819
Coefficient of variation (CV)0.412832580.41671373
Kurtosis1.15669891.1166481
Mean995.03243997.491
Median Absolute Deviation (MAD)288284
Skewness0.901066610.91152712
Sum69652271994982
Variance168741.69172780.05
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:57.984599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 96
 
1.4%
1700 63
 
0.9%
678 26
 
0.4%
682 24
 
0.3%
640 23
 
0.3%
637 22
 
0.3%
632 21
 
0.3%
1304 21
 
0.3%
1356 21
 
0.3%
676 20
 
0.3%
Other values (1007) 6663
95.2%
ValueCountFrequency (%)
1700 30
 
1.5%
850 18
 
0.9%
1356 9
 
0.4%
1366 9
 
0.4%
621 9
 
0.4%
569 8
 
0.4%
1266 8
 
0.4%
670 8
 
0.4%
674 8
 
0.4%
1310 8
 
0.4%
Other values (751) 1885
94.2%
ValueCountFrequency (%)
350 2
< 0.1%
359 1
< 0.1%
365 1
< 0.1%
367 1
< 0.1%
373 1
< 0.1%
376 1
< 0.1%
401 1
< 0.1%
404 1
< 0.1%
405 1
< 0.1%
410 2
< 0.1%
ValueCountFrequency (%)
350 1
0.1%
351 1
0.1%
386 1
0.1%
395 1
0.1%
407 1
0.1%
411 1
0.1%
415 1
0.1%
416 1
0.1%
422 1
0.1%
428 1
0.1%
ValueCountFrequency (%)
350 1
< 0.1%
351 1
< 0.1%
386 1
< 0.1%
395 1
< 0.1%
407 1
< 0.1%
411 1
< 0.1%
415 1
< 0.1%
416 1
< 0.1%
422 1
< 0.1%
428 1
< 0.1%
ValueCountFrequency (%)
350 2
0.1%
359 1
0.1%
365 1
0.1%
367 1
0.1%
373 1
0.1%
376 1
0.1%
401 1
0.1%
404 1
0.1%
405 1
0.1%
410 2
0.1%

age_balance
Real number (ℝ)

 DF1DF2
Distinct44981275
Distinct (%)64.3%63.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean3012420.62998205.4
 DF1DF2
Minimum00
Maximum1379695311946143
Zeros2503726
Zeros (%)35.8%36.3%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:58.059426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum00
5-th percentile00
Q100
median3366175.53455230.8
Q34921123.94886729
95-th percentile7314968.27217582.3
Maximum1379695311946143
Range1379695311946143
Interquartile range (IQR)4921123.94886729

Descriptive statistics

 DF1DF2
Standard deviation2647210.72626962.1
Coefficient of variation (CV)0.878765290.87617816
Kurtosis-0.64606154-0.84466152
Mean3012420.62998205.4
Median Absolute Deviation (MAD)2472185.22369925.7
Skewness0.335066150.27183429
Sum2.1086944 × 10105.9964109 × 109
Variance7.0077244 × 10126.90093 × 1012
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:58.137019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2503
35.8%
7567577.05 1
 
< 0.1%
4740724.6 1
 
< 0.1%
4310990.36 1
 
< 0.1%
6614953.8 1
 
< 0.1%
5180712.67 1
 
< 0.1%
4080906.97 1
 
< 0.1%
3930715.25 1
 
< 0.1%
8865105 1
 
< 0.1%
7403445.98 1
 
< 0.1%
Other values (4488) 4488
64.1%
ValueCountFrequency (%)
0 726
36.3%
5254678.56 1
 
0.1%
3805864.42 1
 
0.1%
7194476.8 1
 
0.1%
4757550.63 1
 
0.1%
5228473.75 1
 
0.1%
2438096.22 1
 
0.1%
5820314.46 1
 
0.1%
5797569.6 1
 
0.1%
5393340.45 1
 
0.1%
Other values (1265) 1265
63.2%
ValueCountFrequency (%)
0 2503
35.8%
150747.6 1
 
< 0.1%
499198 1
 
< 0.1%
573122.74 1
 
< 0.1%
709530.78 1
 
< 0.1%
750575.49 1
 
< 0.1%
804923.13 1
 
< 0.1%
829147.68 1
 
< 0.1%
965392.8 1
 
< 0.1%
965929.2 1
 
< 0.1%
ValueCountFrequency (%)
0 726
36.3%
1268382.05 1
 
0.1%
1329022.76 1
 
0.1%
1391114.25 1
 
0.1%
1462633.04 1
 
0.1%
1476231.22 1
 
0.1%
1485505.28 1
 
0.1%
1545814.39 1
 
0.1%
1562073.75 1
 
0.1%
1586407.09 1
 
0.1%
ValueCountFrequency (%)
0 726
10.4%
1268382.05 1
 
< 0.1%
1329022.76 1
 
< 0.1%
1391114.25 1
 
< 0.1%
1462633.04 1
 
< 0.1%
1476231.22 1
 
< 0.1%
1485505.28 1
 
< 0.1%
1545814.39 1
 
< 0.1%
1562073.75 1
 
< 0.1%
1586407.09 1
 
< 0.1%
ValueCountFrequency (%)
0 2503
125.2%
150747.6 1
 
0.1%
499198 1
 
0.1%
573122.74 1
 
0.1%
709530.78 1
 
0.1%
750575.49 1
 
0.1%
804923.13 1
 
0.1%
829147.68 1
 
0.1%
965392.8 1
 
0.1%
965929.2 1
 
0.1%

engagement_score
Real number (ℝ)

 DF1DF2
Distinct1414
Distinct (%)0.2%0.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.777142860.77235
 DF1DF2
Minimum0.20.2
Maximum1.61.6
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:58.195763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum0.20.2
5-th percentile0.20.2
Q10.50.5
median0.70.7
Q311
95-th percentile1.21.2
Maximum1.61.6
Range1.41.4
Interquartile range (IQR)0.50.5

Descriptive statistics

 DF1DF2
Standard deviation0.307672720.30446153
Coefficient of variation (CV)0.39590240.39420151
Kurtosis-0.93332288-0.90326565
Mean0.777142860.77235
Median Absolute Deviation (MAD)0.30.2
Skewness-0.07934171-0.054009501
Sum54401544.7
Variance0.0946625030.092696826
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:58.246324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.7 1600
22.9%
0.5 1268
18.1%
1 1266
18.1%
1.2 1223
17.5%
0.2 499
 
7.1%
0.9 484
 
6.9%
0.4 434
 
6.2%
0.9 80
 
1.1%
1.4 57
 
0.8%
1.1 36
 
0.5%
Other values (4) 53
 
0.8%
ValueCountFrequency (%)
0.7 466
23.3%
0.5 362
18.1%
1 346
17.3%
1.2 334
16.7%
0.9 156
 
7.8%
0.2 139
 
7.0%
0.4 128
 
6.4%
0.9 20
 
1.0%
1.4 15
 
0.8%
0.6 14
 
0.7%
Other values (4) 20
 
1.0%
ValueCountFrequency (%)
0.2 499
 
7.1%
0.4 434
 
6.2%
0.5 1268
18.1%
0.6 24
 
0.3%
0.7 1600
22.9%
0.8 8
 
0.1%
0.9 484
 
6.9%
0.9 80
 
1.1%
1 1266
18.1%
1.1 36
 
0.5%
ValueCountFrequency (%)
0.2 139
 
7.0%
0.4 128
 
6.4%
0.5 362
18.1%
0.6 14
 
0.7%
0.7 466
23.3%
0.8 1
 
0.1%
0.9 156
 
7.8%
0.9 20
 
1.0%
1 346
17.3%
1.1 14
 
0.7%
ValueCountFrequency (%)
0.2 139
 
2.0%
0.4 128
 
1.8%
0.5 362
5.2%
0.6 14
 
0.2%
0.7 466
6.7%
0.8 1
 
< 0.1%
0.9 156
 
2.2%
0.9 20
 
0.3%
1 346
4.9%
1.1 14
 
0.2%
ValueCountFrequency (%)
0.2 499
 
24.9%
0.4 434
 
21.7%
0.5 1268
63.4%
0.6 24
 
1.2%
0.7 1600
80.0%
0.8 8
 
0.4%
0.9 484
 
24.2%
0.9 80
 
4.0%
1 1266
63.3%
1.1 36
 
1.8%

customer_value_normalized
Real number (ℝ)

 DF1DF2
Distinct70002000
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1.20273861.197587
 DF1DF2
Minimum0.250533370.25345542
Maximum2.53758592.3276345
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:58.386680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum0.250533370.25345542
5-th percentile0.569350220.57264544
Q10.921729490.91463852
median1.1998991.1928979
Q31.4756211.4656813
95-th percentile1.84581691.8412474
Maximum2.53758592.3276345
Range2.28705252.0741791
Interquartile range (IQR)0.553891520.55104274

Descriptive statistics

 DF1DF2
Standard deviation0.389434580.38603944
Coefficient of variation (CV)0.323789870.32234773
Kurtosis-0.389514-0.46498492
Mean1.20273861.197587
Median Absolute Deviation (MAD)0.276709840.27548181
Skewness0.081977220.075895436
Sum8419.17022395.1739
Variance0.151659290.14902645
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:58.463809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.659941081 1
 
< 0.1%
1.085193603 1
 
< 0.1%
1.277455432 1
 
< 0.1%
1.267615586 1
 
< 0.1%
1.322082792 1
 
< 0.1%
0.750240659 1
 
< 0.1%
1.229795576 1
 
< 0.1%
1.02141587 1
 
< 0.1%
1.010570947 1
 
< 0.1%
1.781488283 1
 
< 0.1%
Other values (6990) 6990
99.9%
ValueCountFrequency (%)
0.9715824609 1
 
0.1%
1.107916034 1
 
0.1%
1.461182986 1
 
0.1%
1.694766597 1
 
0.1%
0.9067115869 1
 
0.1%
1.010969852 1
 
0.1%
1.715695287 1
 
0.1%
1.453437381 1
 
0.1%
1.326945298 1
 
0.1%
1.051218096 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.2505333701 1
< 0.1%
0.2508909835 1
< 0.1%
0.2529130595 1
< 0.1%
0.2535260326 1
< 0.1%
0.2540354517 1
< 0.1%
0.2547222276 1
< 0.1%
0.2563143874 1
< 0.1%
0.2636990651 1
< 0.1%
0.2670441409 1
< 0.1%
0.2687053533 1
< 0.1%
ValueCountFrequency (%)
0.2534554237 1
0.1%
0.2535337515 1
0.1%
0.2552535105 1
0.1%
0.2628050849 1
0.1%
0.2687516409 1
0.1%
0.2743172119 1
0.1%
0.2770532809 1
0.1%
0.2883472307 1
0.1%
0.2949296618 1
0.1%
0.3001916254 1
0.1%
ValueCountFrequency (%)
0.2534554237 1
< 0.1%
0.2535337515 1
< 0.1%
0.2552535105 1
< 0.1%
0.2628050849 1
< 0.1%
0.2687516409 1
< 0.1%
0.2743172119 1
< 0.1%
0.2770532809 1
< 0.1%
0.2883472307 1
< 0.1%
0.2949296618 1
< 0.1%
0.3001916254 1
< 0.1%
ValueCountFrequency (%)
0.2505333701 1
0.1%
0.2508909835 1
0.1%
0.2529130595 1
0.1%
0.2535260326 1
0.1%
0.2540354517 1
0.1%
0.2547222276 1
0.1%
0.2563143874 1
0.1%
0.2636990651 1
0.1%
0.2670441409 1
0.1%
0.2687053533 1
0.1%

product_density
Real number (ℝ)

 DF1DF2
Distinct45001278
Distinct (%)64.3%63.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean638142.86646000
 DF1DF2
Minimum4.1948498 × 10-64.6916154 × 10-6
Maximum40000004000000
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:58.545927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum4.1948498 × 10-64.6916154 × 10-6
5-th percentile6.4993284 × 10-66.4329992 × 10-6
Q18.8615179 × 10-68.901386 × 10-6
median1.6330692 × 10-51.6236267 × 10-5
Q320000002000000
95-th percentile20000002000000
Maximum40000004000000
Range40000004000000
Interquartile range (IQR)20000002000000

Descriptive statistics

 DF1DF2
Standard deviation905951.48904484.96
Coefficient of variation (CV)1.41966881.4001315
Kurtosis-0.77815576-0.96291425
Mean638142.86646000
Median Absolute Deviation (MAD)9.1101498 × 10-69.0888596 × 10-6
Skewness0.904658870.85368883
Sum4.467 × 1091.292 × 109
Variance8.2074809 × 10118.1809305 × 1011
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:58.626386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000000 1800
25.7%
1000000 627
 
9.0%
3000000 64
 
0.9%
4000000 12
 
0.2%
9.481032909 × 10-62
 
< 0.1%
1.453569607 × 10-51
 
< 0.1%
6.349250693 × 10-61
 
< 0.1%
8.300016376 × 10-61
 
< 0.1%
7.106263439 × 10-61
 
< 0.1%
1.272033124 × 10-51
 
< 0.1%
Other values (4490) 4490
64.1%
ValueCountFrequency (%)
2000000 523
26.2%
1000000 182
 
9.1%
3000000 20
 
1.0%
9.134716701 × 10-61
 
0.1%
1.602789623 × 10-51
 
0.1%
7.783748778 × 10-61
 
0.1%
1.029941745 × 10-51
 
0.1%
7.84167655 × 10-61
 
0.1%
8.613277781 × 10-61
 
0.1%
6.528856862 × 10-61
 
0.1%
Other values (1268) 1268
63.4%
ValueCountFrequency (%)
4.194849765 × 10-61
< 0.1%
4.49908068 × 10-61
< 0.1%
4.514004247 × 10-61
< 0.1%
4.627275717 × 10-61
< 0.1%
4.701612846 × 10-61
< 0.1%
4.710004327 × 10-61
< 0.1%
4.722008066 × 10-61
< 0.1%
4.773492757 × 10-61
< 0.1%
4.803869305 × 10-61
< 0.1%
4.855264563 × 10-61
< 0.1%
ValueCountFrequency (%)
4.691615426 × 10-61
0.1%
4.701538795 × 10-61
0.1%
4.838777967 × 10-61
0.1%
4.846613175 × 10-61
0.1%
5.140524989 × 10-61
0.1%
5.177970738 × 10-61
0.1%
5.197289904 × 10-61
0.1%
5.214426336 × 10-61
0.1%
5.24743685 × 10-61
0.1%
5.261315327 × 10-61
0.1%
ValueCountFrequency (%)
4.691615426 × 10-61
< 0.1%
4.701538795 × 10-61
< 0.1%
4.838777967 × 10-61
< 0.1%
4.846613175 × 10-61
< 0.1%
5.140524989 × 10-61
< 0.1%
5.177970738 × 10-61
< 0.1%
5.197289904 × 10-61
< 0.1%
5.214426336 × 10-61
< 0.1%
5.24743685 × 10-61
< 0.1%
5.261315327 × 10-61
< 0.1%
ValueCountFrequency (%)
4.194849765 × 10-61
0.1%
4.49908068 × 10-61
0.1%
4.514004247 × 10-61
0.1%
4.627275717 × 10-61
0.1%
4.701612846 × 10-61
0.1%
4.710004327 × 10-61
0.1%
4.722008066 × 10-61
0.1%
4.773492757 × 10-61
0.1%
4.803869305 × 10-61
0.1%
4.855264563 × 10-61
0.1%

balance_salary_ratio
Real number (ℝ)

 DF1DF2
Distinct44981275
Distinct (%)64.3%63.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean4.46166062.5059504
 DF1DF2
Minimum00
Maximum10614.655437.98084
Zeros2503726
Zeros (%)35.8%36.3%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:58.707558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum00
5-th percentile00
Q100
median0.755239550.74294631
Q31.51587961.5214014
95-th percentile7.10346086.924535
Maximum10614.655437.98084
Range10614.655437.98084
Interquartile range (IQR)1.51587961.5214014

Descriptive statistics

 DF1DF2
Standard deviation129.1640415.176226
Coefficient of variation (CV)28.9497676.0560758
Kurtosis6510.1802460.14466
Mean4.46166062.5059504
Median Absolute Deviation (MAD)0.755239550.74294631
Skewness79.44816919.56743
Sum31231.6245011.9008
Variance16683.348230.31782
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:58.785256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2503
35.8%
1.180563256 1
 
< 0.1%
3.004457179 1
 
< 0.1%
5.160714304 1
 
< 0.1%
1.914260698 1
 
< 0.1%
1.269898834 1
 
< 0.1%
3.885045435 1
 
< 0.1%
1.264069338 1
 
< 0.1%
1.675075199 1
 
< 0.1%
10.81682866 1
 
< 0.1%
Other values (4488) 4488
64.1%
ValueCountFrequency (%)
0 726
36.3%
0.6956886263 1
 
0.1%
0.3242212101 1
 
0.1%
0.6889474949 1
 
0.1%
1.298532644 1
 
0.1%
0.6662076524 1
 
0.1%
0.7839940975 1
 
0.1%
1.642326769 1
 
0.1%
1.888980536 1
 
0.1%
1.622824862 1
 
0.1%
Other values (1265) 1265
63.2%
ValueCountFrequency (%)
0 2503
35.8%
0.02128418881 1
 
< 0.1%
0.07946553593 1
 
< 0.1%
0.1383670781 1
 
< 0.1%
0.1416141163 1
 
< 0.1%
0.1809964844 1
 
< 0.1%
0.1875142498 1
 
< 0.1%
0.2009926374 1
 
< 0.1%
0.2053787413 1
 
< 0.1%
0.2150868188 1
 
< 0.1%
ValueCountFrequency (%)
0 726
36.3%
0.1925817779 1
 
0.1%
0.2618388929 1
 
0.1%
0.268163615 1
 
0.1%
0.295163969 1
 
0.1%
0.3086059021 1
 
0.1%
0.3087132841 1
 
0.1%
0.3162846396 1
 
0.1%
0.3220743842 1
 
0.1%
0.3241764136 1
 
0.1%
ValueCountFrequency (%)
0 726
10.4%
0.1925817779 1
 
< 0.1%
0.2618388929 1
 
< 0.1%
0.268163615 1
 
< 0.1%
0.295163969 1
 
< 0.1%
0.3086059021 1
 
< 0.1%
0.3087132841 1
 
< 0.1%
0.3162846396 1
 
< 0.1%
0.3220743842 1
 
< 0.1%
0.3241764136 1
 
< 0.1%
ValueCountFrequency (%)
0 2503
125.2%
0.02128418881 1
 
0.1%
0.07946553593 1
 
0.1%
0.1383670781 1
 
0.1%
0.1416141163 1
 
0.1%
0.1809964844 1
 
0.1%
0.1875142498 1
 
0.1%
0.2009926374 1
 
0.1%
0.2053787413 1
 
0.1%
0.2150868188 1
 
0.1%

credit_score_age_ratio
Real number (ℝ)

 DF1DF2
Distinct48001751
Distinct (%)68.6%87.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean17.83555417.882988
 DF1DF2
Minimum5.82954556.1578947
Maximum46.88888944.777778
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:58.858933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum5.82954556.1578947
5-th percentile10.18390410.265987
Q114.07973714.110714
median17.27517.211111
Q320.90080621.027215
95-th percentile27.41935527.798237
Maximum46.88888944.777778
Range41.05934338.619883
Interquartile range (IQR)6.82106966.9165004

Descriptive statistics

 DF1DF2
Standard deviation5.3386485.3806327
Coefficient of variation (CV)0.299326160.30087995
Kurtosis1.05592630.95076304
Mean17.83555417.882988
Median Absolute Deviation (MAD)3.38490343.3643054
Skewness0.755754220.7828046
Sum124848.8835765.977
Variance28.50116328.951208
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:58.930813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 17
 
0.2%
20 17
 
0.2%
22 14
 
0.2%
16 13
 
0.2%
25 13
 
0.2%
21.25 13
 
0.2%
14 12
 
0.2%
18.5 12
 
0.2%
13 12
 
0.2%
19 12
 
0.2%
Other values (4790) 6865
98.1%
ValueCountFrequency (%)
17 6
 
0.3%
16 6
 
0.3%
14.25 5
 
0.2%
18 5
 
0.2%
17.7027027 4
 
0.2%
21.5 4
 
0.2%
20.3030303 4
 
0.2%
23.61111111 4
 
0.2%
26.5625 4
 
0.2%
17.33333333 4
 
0.2%
Other values (1741) 1954
97.7%
ValueCountFrequency (%)
5.829545455 1
< 0.1%
5.833333333 1
< 0.1%
6.112676056 1
< 0.1%
6.22972973 1
< 0.1%
6.392857143 1
< 0.1%
6.821917808 1
< 0.1%
6.826666667 1
< 0.1%
6.841269841 1
< 0.1%
6.862745098 1
< 0.1%
6.884615385 1
< 0.1%
ValueCountFrequency (%)
6.157894737 1
0.1%
6.651515152 1
0.1%
6.766233766 1
0.1%
6.813333333 1
0.1%
6.819444444 1
0.1%
6.898550725 1
0.1%
6.903225806 1
0.1%
7.283018868 1
0.1%
7.3 1
0.1%
7.814814815 1
0.1%
ValueCountFrequency (%)
6.157894737 1
< 0.1%
6.651515152 1
< 0.1%
6.766233766 1
< 0.1%
6.813333333 1
< 0.1%
6.819444444 1
< 0.1%
6.898550725 1
< 0.1%
6.903225806 1
< 0.1%
7.283018868 1
< 0.1%
7.3 1
< 0.1%
7.814814815 1
< 0.1%
ValueCountFrequency (%)
5.829545455 1
0.1%
5.833333333 1
0.1%
6.112676056 1
0.1%
6.22972973 1
0.1%
6.392857143 1
0.1%
6.821917808 1
0.1%
6.826666667 1
0.1%
6.841269841 1
0.1%
6.862745098 1
0.1%
6.884615385 1
0.1%

tenure_age_ratio
Real number (ℝ)

 DF1DF2
Distinct401323
Distinct (%)5.7%16.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.13783580.13843236
 DF1DF2
Minimum00
Maximum0.555555560.47368421
Zeros29778
Zeros (%)4.2%3.9%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:59.002294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum00
5-th percentile0.0172413790.018867925
Q10.0645161290.0625
median0.129032260.13157895
Q30.20.2
95-th percentile0.291666670.3
Maximum0.555555560.47368421
Range0.555555560.47368421
Interquartile range (IQR)0.135483870.1375

Descriptive statistics

 DF1DF2
Standard deviation0.0892284340.090599974
Coefficient of variation (CV)0.647353120.65447107
Kurtosis-0.059929341-0.070866567
Mean0.13783580.13843236
Median Absolute Deviation (MAD)0.0666199160.068421053
Skewness0.551285840.57593009
Sum964.85058276.86472
Variance0.00796171330.0082083553
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:59.076187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 297
 
4.2%
0.2 132
 
1.9%
0.1666666667 103
 
1.5%
0.25 103
 
1.5%
0.1428571429 100
 
1.4%
0.125 86
 
1.2%
0.1111111111 76
 
1.1%
0.1538461538 73
 
1.0%
0.1 71
 
1.0%
0.1818181818 58
 
0.8%
Other values (391) 5901
84.3%
ValueCountFrequency (%)
0 78
 
3.9%
0.2 38
 
1.9%
0.1666666667 37
 
1.8%
0.1538461538 29
 
1.5%
0.1428571429 28
 
1.4%
0.25 26
 
1.3%
0.125 25
 
1.2%
0.1333333333 21
 
1.1%
0.1111111111 21
 
1.1%
0.05263157895 21
 
1.1%
Other values (313) 1676
83.8%
ValueCountFrequency (%)
0 297
4.2%
0.01086956522 1
 
< 0.1%
0.01234567901 1
 
< 0.1%
0.01298701299 1
 
< 0.1%
0.01333333333 1
 
< 0.1%
0.01351351351 1
 
< 0.1%
0.01369863014 1
 
< 0.1%
0.01388888889 2
 
< 0.1%
0.01408450704 2
 
< 0.1%
0.01428571429 4
 
0.1%
ValueCountFrequency (%)
0 78
3.9%
0.01369863014 1
 
0.1%
0.01449275362 1
 
0.1%
0.01492537313 2
 
0.1%
0.01515151515 1
 
0.1%
0.01538461538 1
 
0.1%
0.01587301587 1
 
0.1%
0.01612903226 1
 
0.1%
0.01639344262 1
 
0.1%
0.01666666667 1
 
0.1%
ValueCountFrequency (%)
0 78
1.1%
0.01369863014 1
 
< 0.1%
0.01449275362 1
 
< 0.1%
0.01492537313 2
 
< 0.1%
0.01515151515 1
 
< 0.1%
0.01538461538 1
 
< 0.1%
0.01587301587 1
 
< 0.1%
0.01612903226 1
 
< 0.1%
0.01639344262 1
 
< 0.1%
0.01666666667 1
 
< 0.1%
ValueCountFrequency (%)
0 297
14.8%
0.01086956522 1
 
0.1%
0.01234567901 1
 
0.1%
0.01298701299 1
 
0.1%
0.01333333333 1
 
0.1%
0.01351351351 1
 
0.1%
0.01369863014 1
 
0.1%
0.01388888889 2
 
0.1%
0.01408450704 2
 
0.1%
0.01428571429 4
 
0.2%

credit_salary_ratio
Real number (ℝ)

 DF1DF2
Distinct70002000
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0353023620.033129809
 DF1DF2
Minimum0.00217395520.0020310907
Maximum61.2262527.3750909
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:05:59.221151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum0.00217395520.0020310907
5-th percentile0.00316272170.0031115148
Q10.00437130640.0042913139
median0.00650486740.0063196517
Q30.0129062150.012438387
95-th percentile0.0643548250.067699051
Maximum61.2262527.3750909
Range61.2240787.3730598
Interquartile range (IQR)0.00853490840.0081470733

Descriptive statistics

 DF1DF2
Standard deviation0.756009880.24115527
Coefficient of variation (CV)21.4152777.2791024
Kurtosis6137.6679540.66324
Mean0.0353023620.033129809
Median Absolute Deviation (MAD)0.00269693510.0026415822
Skewness76.20886920.909804
Sum247.1165466.259618
Variance0.571550950.058155865
MonotonicityNot monotonicNot monotonic
2024-04-29T14:05:59.298829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004710504734 1
 
< 0.1%
0.006715978698 1
 
< 0.1%
0.003781709441 1
 
< 0.1%
0.02621064541 1
 
< 0.1%
0.006429530041 1
 
< 0.1%
0.01488623024 1
 
< 0.1%
0.006461131027 1
 
< 0.1%
0.01866311368 1
 
< 0.1%
0.004707167195 1
 
< 0.1%
0.00537855284 1
 
< 0.1%
Other values (6990) 6990
99.9%
ValueCountFrequency (%)
0.006204718153 1
 
0.1%
0.02454826486 1
 
0.1%
0.003086770843 1
 
0.1%
0.00313711762 1
 
0.1%
0.01030579051 1
 
0.1%
0.008773429135 1
 
0.1%
0.003636032708 1
 
0.1%
0.003849072596 1
 
0.1%
0.00868523828 1
 
0.1%
0.00939933701 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.002173955152 1
< 0.1%
0.002207422047 1
< 0.1%
0.002207951468 1
< 0.1%
0.002263401272 1
< 0.1%
0.002281603985 1
< 0.1%
0.002283498674 1
< 0.1%
0.002283542134 1
< 0.1%
0.002302221874 1
< 0.1%
0.002323792681 1
< 0.1%
0.002330801574 1
< 0.1%
ValueCountFrequency (%)
0.002031090659 1
0.1%
0.002069310829 1
0.1%
0.002529939561 1
0.1%
0.002531914066 1
0.1%
0.0025390081 1
0.1%
0.002554601072 1
0.1%
0.002564842953 1
0.1%
0.002583251906 1
0.1%
0.002584503939 1
0.1%
0.002591523612 1
0.1%
ValueCountFrequency (%)
0.002031090659 1
< 0.1%
0.002069310829 1
< 0.1%
0.002529939561 1
< 0.1%
0.002531914066 1
< 0.1%
0.0025390081 1
< 0.1%
0.002554601072 1
< 0.1%
0.002564842953 1
< 0.1%
0.002583251906 1
< 0.1%
0.002584503939 1
< 0.1%
0.002591523612 1
< 0.1%
ValueCountFrequency (%)
0.002173955152 1
0.1%
0.002207422047 1
0.1%
0.002207951468 1
0.1%
0.002263401272 1
0.1%
0.002281603985 1
0.1%
0.002283498674 1
0.1%
0.002283542134 1
0.1%
0.002302221874 1
0.1%
0.002323792681 1
0.1%
0.002330801574 1
0.1%
 DF1DF2
Distinct22
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
low
6322 
high
678 
low
1800 
high
200 

Length

 DF1DF2
Max length44
Median length33
Mean length3.09685713.1
Min length33

Characters and Unicode

 DF1DF2
Total characters216786200
Distinct characters66
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st rowlowlow
2nd rowlowlow
3rd rowlowlow
4th rowlowlow
5th rowhighlow

Common Values

ValueCountFrequency (%)
low 6322
90.3%
high 678
 
9.7%
ValueCountFrequency (%)
low 1800
90.0%
high 200
 
10.0%

Length

2024-04-29T14:05:59.360371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:59.401748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:59.440788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
low 6322
90.3%
high 678
 
9.7%
ValueCountFrequency (%)
low 1800
90.0%
high 200
 
10.0%

Most occurring characters

ValueCountFrequency (%)
l 6322
29.2%
o 6322
29.2%
w 6322
29.2%
h 1356
 
6.3%
i 678
 
3.1%
g 678
 
3.1%
ValueCountFrequency (%)
l 1800
29.0%
o 1800
29.0%
w 1800
29.0%
h 400
 
6.5%
i 200
 
3.2%
g 200
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21678
100.0%
ValueCountFrequency (%)
(unknown) 6200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 6322
29.2%
o 6322
29.2%
w 6322
29.2%
h 1356
 
6.3%
i 678
 
3.1%
g 678
 
3.1%
ValueCountFrequency (%)
l 1800
29.0%
o 1800
29.0%
w 1800
29.0%
h 400
 
6.5%
i 200
 
3.2%
g 200
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21678
100.0%
ValueCountFrequency (%)
(unknown) 6200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 6322
29.2%
o 6322
29.2%
w 6322
29.2%
h 1356
 
6.3%
i 678
 
3.1%
g 678
 
3.1%
ValueCountFrequency (%)
l 1800
29.0%
o 1800
29.0%
w 1800
29.0%
h 400
 
6.5%
i 200
 
3.2%
g 200
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21678
100.0%
ValueCountFrequency (%)
(unknown) 6200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 6322
29.2%
o 6322
29.2%
w 6322
29.2%
h 1356
 
6.3%
i 678
 
3.1%
g 678
 
3.1%
ValueCountFrequency (%)
l 1800
29.0%
o 1800
29.0%
w 1800
29.0%
h 400
 
6.5%
i 200
 
3.2%
g 200
 
3.2%

life_stage
Categorical

 DF1DF2
Distinct44
Distinct (%)0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Memory size61.7 KiB17.8 KiB
middle_age
3224 
adulthood
2816 
senior
901 
adolescence
 
59
middle_age
935 
adulthood
805 
senior
241 
adolescence
 
19

Length

 DF1DF2
Max length1111
Median length1010
Mean length9.09128579.125
Min length66

Characters and Unicode

 DF1DF2
Total characters6363918250
Distinct characters1616
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st rowseniormiddle_age
2nd rowmiddle_ageadulthood
3rd rowadulthoodmiddle_age
4th rowadulthoodadulthood
5th rowadulthoodadulthood

Common Values

ValueCountFrequency (%)
middle_age 3224
46.1%
adulthood 2816
40.2%
senior 901
 
12.9%
adolescence 59
 
0.8%
ValueCountFrequency (%)
middle_age 935
46.8%
adulthood 805
40.2%
senior 241
 
12.0%
adolescence 19
 
0.9%

Length

2024-04-29T14:05:59.487682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:59.532993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:59.579291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
middle_age 3224
46.1%
adulthood 2816
40.2%
senior 901
 
12.9%
adolescence 59
 
0.8%
ValueCountFrequency (%)
middle_age 935
46.8%
adulthood 805
40.2%
senior 241
 
12.0%
adolescence 19
 
0.9%

Most occurring characters

ValueCountFrequency (%)
d 12139
19.1%
e 7526
11.8%
o 6592
10.4%
l 6099
9.6%
a 6099
9.6%
i 4125
 
6.5%
m 3224
 
5.1%
_ 3224
 
5.1%
g 3224
 
5.1%
u 2816
 
4.4%
Other values (6) 8571
13.5%
ValueCountFrequency (%)
d 3499
19.2%
e 2168
11.9%
o 1870
10.2%
l 1759
9.6%
a 1759
9.6%
i 1176
 
6.4%
m 935
 
5.1%
_ 935
 
5.1%
g 935
 
5.1%
u 805
 
4.4%
Other values (6) 2409
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63639
100.0%
ValueCountFrequency (%)
(unknown) 18250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 12139
19.1%
e 7526
11.8%
o 6592
10.4%
l 6099
9.6%
a 6099
9.6%
i 4125
 
6.5%
m 3224
 
5.1%
_ 3224
 
5.1%
g 3224
 
5.1%
u 2816
 
4.4%
Other values (6) 8571
13.5%
ValueCountFrequency (%)
d 3499
19.2%
e 2168
11.9%
o 1870
10.2%
l 1759
9.6%
a 1759
9.6%
i 1176
 
6.4%
m 935
 
5.1%
_ 935
 
5.1%
g 935
 
5.1%
u 805
 
4.4%
Other values (6) 2409
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63639
100.0%
ValueCountFrequency (%)
(unknown) 18250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 12139
19.1%
e 7526
11.8%
o 6592
10.4%
l 6099
9.6%
a 6099
9.6%
i 4125
 
6.5%
m 3224
 
5.1%
_ 3224
 
5.1%
g 3224
 
5.1%
u 2816
 
4.4%
Other values (6) 8571
13.5%
ValueCountFrequency (%)
d 3499
19.2%
e 2168
11.9%
o 1870
10.2%
l 1759
9.6%
a 1759
9.6%
i 1176
 
6.4%
m 935
 
5.1%
_ 935
 
5.1%
g 935
 
5.1%
u 805
 
4.4%
Other values (6) 2409
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63639
100.0%
ValueCountFrequency (%)
(unknown) 18250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 12139
19.1%
e 7526
11.8%
o 6592
10.4%
l 6099
9.6%
a 6099
9.6%
i 4125
 
6.5%
m 3224
 
5.1%
_ 3224
 
5.1%
g 3224
 
5.1%
u 2816
 
4.4%
Other values (6) 8571
13.5%
ValueCountFrequency (%)
d 3499
19.2%
e 2168
11.9%
o 1870
10.2%
l 1759
9.6%
a 1759
9.6%
i 1176
 
6.4%
m 935
 
5.1%
_ 935
 
5.1%
g 935
 
5.1%
u 805
 
4.4%
Other values (6) 2409
13.2%

cs_category
Categorical

 DF1DF2
Distinct33
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size61.7 KiB17.7 KiB
medium
4336 
high
2203 
low
461 
medium
1249 
high
623 
low
128 

Length

 DF1DF2
Max length66
Median length66
Mean length5.1735.185
Min length33

Characters and Unicode

 DF1DF2
Total characters3621110370
Distinct characters1010
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st rowmediummedium
2nd rowmediummedium
3rd rowhighmedium
4th rowmediummedium
5th rowmediummedium

Common Values

ValueCountFrequency (%)
medium 4336
61.9%
high 2203
31.5%
low 461
 
6.6%
ValueCountFrequency (%)
medium 1249
62.5%
high 623
31.1%
low 128
 
6.4%

Length

2024-04-29T14:05:59.631465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:59.674231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:59.716082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
medium 4336
61.9%
high 2203
31.5%
low 461
 
6.6%
ValueCountFrequency (%)
medium 1249
62.5%
high 623
31.1%
low 128
 
6.4%

Most occurring characters

ValueCountFrequency (%)
m 8672
23.9%
i 6539
18.1%
h 4406
12.2%
e 4336
12.0%
d 4336
12.0%
u 4336
12.0%
g 2203
 
6.1%
l 461
 
1.3%
o 461
 
1.3%
w 461
 
1.3%
ValueCountFrequency (%)
m 2498
24.1%
i 1872
18.1%
e 1249
12.0%
d 1249
12.0%
u 1249
12.0%
h 1246
12.0%
g 623
 
6.0%
l 128
 
1.2%
o 128
 
1.2%
w 128
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36211
100.0%
ValueCountFrequency (%)
(unknown) 10370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 8672
23.9%
i 6539
18.1%
h 4406
12.2%
e 4336
12.0%
d 4336
12.0%
u 4336
12.0%
g 2203
 
6.1%
l 461
 
1.3%
o 461
 
1.3%
w 461
 
1.3%
ValueCountFrequency (%)
m 2498
24.1%
i 1872
18.1%
e 1249
12.0%
d 1249
12.0%
u 1249
12.0%
h 1246
12.0%
g 623
 
6.0%
l 128
 
1.2%
o 128
 
1.2%
w 128
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36211
100.0%
ValueCountFrequency (%)
(unknown) 10370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 8672
23.9%
i 6539
18.1%
h 4406
12.2%
e 4336
12.0%
d 4336
12.0%
u 4336
12.0%
g 2203
 
6.1%
l 461
 
1.3%
o 461
 
1.3%
w 461
 
1.3%
ValueCountFrequency (%)
m 2498
24.1%
i 1872
18.1%
e 1249
12.0%
d 1249
12.0%
u 1249
12.0%
h 1246
12.0%
g 623
 
6.0%
l 128
 
1.2%
o 128
 
1.2%
w 128
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36211
100.0%
ValueCountFrequency (%)
(unknown) 10370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 8672
23.9%
i 6539
18.1%
h 4406
12.2%
e 4336
12.0%
d 4336
12.0%
u 4336
12.0%
g 2203
 
6.1%
l 461
 
1.3%
o 461
 
1.3%
w 461
 
1.3%
ValueCountFrequency (%)
m 2498
24.1%
i 1872
18.1%
e 1249
12.0%
d 1249
12.0%
u 1249
12.0%
h 1246
12.0%
g 623
 
6.0%
l 128
 
1.2%
o 128
 
1.2%
w 128
 
1.2%

tenure_group
Categorical

 DF1DF2
Distinct33
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size61.7 KiB17.7 KiB
long_standing
2499 
new
2452 
intermediate
2049 
new
688 
long_standing
687 
intermediate
625 

Length

 DF1DF2
Max length1313
Median length1212
Mean length9.20442869.2475
Min length33

Characters and Unicode

 DF1DF2
Total characters6443118495
Distinct characters1414
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st rownewlong_standing
2nd rowlong_standingintermediate
3rd rowlong_standingintermediate
4th rowlong_standingintermediate
5th rowlong_standingnew

Common Values

ValueCountFrequency (%)
long_standing 2499
35.7%
new 2452
35.0%
intermediate 2049
29.3%
ValueCountFrequency (%)
new 688
34.4%
long_standing 687
34.4%
intermediate 625
31.2%

Length

2024-04-29T14:05:59.761234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:59.803430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:59.846494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
long_standing 2499
35.7%
new 2452
35.0%
intermediate 2049
29.3%
ValueCountFrequency (%)
new 688
34.4%
long_standing 687
34.4%
intermediate 625
31.2%

Most occurring characters

ValueCountFrequency (%)
n 11998
18.6%
e 8599
13.3%
t 6597
10.2%
i 6597
10.2%
g 4998
7.8%
a 4548
 
7.1%
d 4548
 
7.1%
l 2499
 
3.9%
o 2499
 
3.9%
_ 2499
 
3.9%
Other values (4) 9049
14.0%
ValueCountFrequency (%)
n 3374
18.2%
e 2563
13.9%
t 1937
10.5%
i 1937
10.5%
g 1374
7.4%
a 1312
 
7.1%
d 1312
 
7.1%
w 688
 
3.7%
l 687
 
3.7%
o 687
 
3.7%
Other values (4) 2624
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64431
100.0%
ValueCountFrequency (%)
(unknown) 18495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 11998
18.6%
e 8599
13.3%
t 6597
10.2%
i 6597
10.2%
g 4998
7.8%
a 4548
 
7.1%
d 4548
 
7.1%
l 2499
 
3.9%
o 2499
 
3.9%
_ 2499
 
3.9%
Other values (4) 9049
14.0%
ValueCountFrequency (%)
n 3374
18.2%
e 2563
13.9%
t 1937
10.5%
i 1937
10.5%
g 1374
7.4%
a 1312
 
7.1%
d 1312
 
7.1%
w 688
 
3.7%
l 687
 
3.7%
o 687
 
3.7%
Other values (4) 2624
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64431
100.0%
ValueCountFrequency (%)
(unknown) 18495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 11998
18.6%
e 8599
13.3%
t 6597
10.2%
i 6597
10.2%
g 4998
7.8%
a 4548
 
7.1%
d 4548
 
7.1%
l 2499
 
3.9%
o 2499
 
3.9%
_ 2499
 
3.9%
Other values (4) 9049
14.0%
ValueCountFrequency (%)
n 3374
18.2%
e 2563
13.9%
t 1937
10.5%
i 1937
10.5%
g 1374
7.4%
a 1312
 
7.1%
d 1312
 
7.1%
w 688
 
3.7%
l 687
 
3.7%
o 687
 
3.7%
Other values (4) 2624
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64431
100.0%
ValueCountFrequency (%)
(unknown) 18495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 11998
18.6%
e 8599
13.3%
t 6597
10.2%
i 6597
10.2%
g 4998
7.8%
a 4548
 
7.1%
d 4548
 
7.1%
l 2499
 
3.9%
o 2499
 
3.9%
_ 2499
 
3.9%
Other values (4) 9049
14.0%
ValueCountFrequency (%)
n 3374
18.2%
e 2563
13.9%
t 1937
10.5%
i 1937
10.5%
g 1374
7.4%
a 1312
 
7.1%
d 1312
 
7.1%
w 688
 
3.7%
l 687
 
3.7%
o 687
 
3.7%
Other values (4) 2624
14.2%
 DF1DF2
Distinct22
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
650.1
3850 
652.6323809523809
3150 
652.5271028037383
1070 
646.305376344086
930 

Length

 DF1DF2
Max length1717
Median length517
Mean length10.416.535
Min length516

Characters and Unicode

 DF1DF2
Total characters7280033070
Distinct characters910
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 DF1DF2
Unique00 ?
Unique (%)0.0%0.0%

Sample

 DF1DF2
1st row652.6323809523809652.5271028037383
2nd row650.1652.5271028037383
3rd row652.6323809523809646.305376344086
4th row650.1652.5271028037383
5th row652.6323809523809646.305376344086

Common Values

ValueCountFrequency (%)
650.1 3850
55.0%
652.6323809523809 3150
45.0%
ValueCountFrequency (%)
652.5271028037383 1070
53.5%
646.305376344086 930
46.5%

Length

2024-04-29T14:05:59.893713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

DF1

2024-04-29T14:05:59.934733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:59.973834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
650.1 3850
55.0%
652.6323809523809 3150
45.0%
ValueCountFrequency (%)
652.5271028037383 1070
53.5%
646.305376344086 930
46.5%

Most occurring characters

ValueCountFrequency (%)
6 10150
13.9%
5 10150
13.9%
0 10150
13.9%
2 9450
13.0%
3 9450
13.0%
. 7000
9.6%
8 6300
8.7%
9 6300
8.7%
1 3850
 
5.3%
ValueCountFrequency (%)
3 6000
18.1%
6 4790
14.5%
0 4000
12.1%
2 3210
9.7%
5 3070
9.3%
7 3070
9.3%
8 3070
9.3%
4 2790
8.4%
. 2000
 
6.0%
1 1070
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72800
100.0%
ValueCountFrequency (%)
(unknown) 33070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 10150
13.9%
5 10150
13.9%
0 10150
13.9%
2 9450
13.0%
3 9450
13.0%
. 7000
9.6%
8 6300
8.7%
9 6300
8.7%
1 3850
 
5.3%
ValueCountFrequency (%)
3 6000
18.1%
6 4790
14.5%
0 4000
12.1%
2 3210
9.7%
5 3070
9.3%
7 3070
9.3%
8 3070
9.3%
4 2790
8.4%
. 2000
 
6.0%
1 1070
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72800
100.0%
ValueCountFrequency (%)
(unknown) 33070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 10150
13.9%
5 10150
13.9%
0 10150
13.9%
2 9450
13.0%
3 9450
13.0%
. 7000
9.6%
8 6300
8.7%
9 6300
8.7%
1 3850
 
5.3%
ValueCountFrequency (%)
3 6000
18.1%
6 4790
14.5%
0 4000
12.1%
2 3210
9.7%
5 3070
9.3%
7 3070
9.3%
8 3070
9.3%
4 2790
8.4%
. 2000
 
6.0%
1 1070
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72800
100.0%
ValueCountFrequency (%)
(unknown) 33070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 10150
13.9%
5 10150
13.9%
0 10150
13.9%
2 9450
13.0%
3 9450
13.0%
. 7000
9.6%
8 6300
8.7%
9 6300
8.7%
1 3850
 
5.3%
ValueCountFrequency (%)
3 6000
18.1%
6 4790
14.5%
0 4000
12.1%
2 3210
9.7%
5 3070
9.3%
7 3070
9.3%
8 3070
9.3%
4 2790
8.4%
. 2000
 
6.0%
1 1070
 
3.2%

balance_age
Real number (ℝ)

 DF1DF2
Distinct6762
Distinct (%)1.0%3.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean76766.16476904.616
 DF1DF2
Minimum00
Maximum123794.77141020.62
Zeros51
Zeros (%)0.1%< 0.1%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:06:00.034953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum00
5-th percentile69000.61660838.194
Q173370.24274373.372
median76651.24476814.925
Q380281.22182075.146
95-th percentile86132.50690635.463
Maximum123794.77141020.62
Range123794.77141020.62
Interquartile range (IQR)6910.97867701.7739

Descriptive statistics

 DF1DF2
Standard deviation6622.107411185.557
Coefficient of variation (CV)0.0862633620.14544715
Kurtosis16.8029996.11845
Mean76766.16476904.616
Median Absolute Deviation (MAD)3629.97623879.5333
Skewness-0.89094316-0.43608796
Sum5.3736315 × 1081.5380923 × 108
Variance438523061.2511668 × 108
MonotonicityNot monotonicNot monotonic
2024-04-29T14:06:00.112079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80281.22056 342
 
4.9%
74039.61268 340
 
4.9%
82639.83869 337
 
4.8%
75116.59748 321
 
4.6%
69000.61583 319
 
4.6%
69137.2785 314
 
4.5%
76651.24439 305
 
4.4%
73823.82703 300
 
4.3%
77056.26076 289
 
4.1%
77118.50422 277
 
4.0%
Other values (57) 3856
55.1%
ValueCountFrequency (%)
83660.8401 102
 
5.1%
78806.42747 99
 
5.0%
80080.02258 93
 
4.7%
75995.65717 92
 
4.6%
74783.13791 91
 
4.5%
74842.03568 88
 
4.4%
68951.741 80
 
4.0%
82075.14632 76
 
3.8%
80612.25473 74
 
3.7%
76814.92548 73
 
3.6%
Other values (52) 1132
56.6%
ValueCountFrequency (%)
0 5
 
0.1%
30507.2875 4
 
0.1%
42461.39833 6
 
0.1%
51953.56833 6
 
0.1%
52355.81154 13
 
0.2%
55732.22789 19
 
0.3%
62007.52765 17
 
0.2%
65501.9572 25
0.4%
65782.50688 16
 
0.2%
68935.45569 58
0.8%
ValueCountFrequency (%)
0 1
 
0.1%
17616.826 5
 
0.2%
26975.34 3
 
0.1%
41099.196 20
1.0%
41686.17571 7
 
0.4%
48096.68375 8
 
0.4%
52569.84182 11
0.5%
58662.77 2
 
0.1%
59296.26429 7
 
0.4%
60603.48 2
 
0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
17616.826 5
 
0.1%
26975.34 3
 
< 0.1%
41099.196 20
0.3%
41686.17571 7
 
0.1%
48096.68375 8
 
0.1%
52569.84182 11
0.2%
58662.77 2
 
< 0.1%
59296.26429 7
 
0.1%
60603.48 2
 
< 0.1%
ValueCountFrequency (%)
0 5
 
0.2%
30507.2875 4
 
0.2%
42461.39833 6
 
0.3%
51953.56833 6
 
0.3%
52355.81154 13
 
0.7%
55732.22789 19
 
0.9%
62007.52765 17
 
0.9%
65501.9572 25
1.2%
65782.50688 16
 
0.8%
68935.45569 58
2.9%

salary_rank_geography
Real number (ℝ)

 DF1DF2
Distinct35001021
Distinct (%)50.0%51.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1313.0036381.0205
 DF1DF2
Minimum11
Maximum35001021
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size109.4 KiB31.2 KiB
2024-04-29T14:06:00.189554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

 DF1DF2
Minimum11
5-th percentile11734
Q1584167
median1167334
Q31753521.25
95-th percentile3150.05921.05
Maximum35001021
Range34991020
Interquartile range (IQR)1169354.25

Descriptive statistics

 DF1DF2
Standard deviation910.7956268.00749
Coefficient of variation (CV)0.693673360.70339388
Kurtosis-0.43261615-0.4419552
Mean1313.0036381.0205
Median Absolute Deviation (MAD)584172
Skewness0.665277580.68731449
Sum9191025762041
Variance829548.6371828.013
MonotonicityNot monotonicNot monotonic
2024-04-29T14:06:00.267092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1019 3
 
< 0.1%
511 3
 
< 0.1%
1374 3
 
< 0.1%
624 3
 
< 0.1%
1627 3
 
< 0.1%
1438 3
 
< 0.1%
960 3
 
< 0.1%
1622 3
 
< 0.1%
1276 3
 
< 0.1%
795 3
 
< 0.1%
Other values (3490) 6970
99.6%
ValueCountFrequency (%)
246 3
 
0.1%
366 3
 
0.1%
180 3
 
0.1%
437 3
 
0.1%
177 3
 
0.1%
139 3
 
0.1%
455 3
 
0.1%
340 3
 
0.1%
262 3
 
0.1%
321 3
 
0.1%
Other values (1011) 1970
98.5%
ValueCountFrequency (%)
1 3
< 0.1%
2 3
< 0.1%
3 3
< 0.1%
4 3
< 0.1%
5 3
< 0.1%
6 3
< 0.1%
7 3
< 0.1%
8 3
< 0.1%
9 3
< 0.1%
10 3
< 0.1%
ValueCountFrequency (%)
1 3
0.1%
2 3
0.1%
3 3
0.1%
4 3
0.1%
5 3
0.1%
6 3
0.1%
7 3
0.1%
8 3
0.1%
9 3
0.1%
10 3
0.1%
ValueCountFrequency (%)
1 3
< 0.1%
2 3
< 0.1%
3 3
< 0.1%
4 3
< 0.1%
5 3
< 0.1%
6 3
< 0.1%
7 3
< 0.1%
8 3
< 0.1%
9 3
< 0.1%
10 3
< 0.1%
ValueCountFrequency (%)
1 3
0.1%
2 3
0.1%
3 3
0.1%
4 3
0.1%
5 3
0.1%
6 3
0.1%
7 3
0.1%
8 3
0.1%
9 3
0.1%
10 3
0.1%

Interactions

DF1

2024-04-29T14:05:37.678005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:54.759104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:21.804812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:39.044292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:22.742479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:39.874385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:23.628463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:40.718950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:24.415397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:41.618084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:25.216083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:42.440981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:26.147558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:43.378314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:26.999531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:44.213408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:27.870192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:45.083724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:28.651492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:45.916199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:29.491568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:46.760200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:30.439561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:47.586639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:31.351396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:48.536314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:32.328726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:49.312701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:33.238936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:50.344233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:34.267363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:51.190867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:35.140244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:52.056671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:35.953395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.028262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:36.855182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.849072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:37.719092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:54.797494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:21.926362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:39.087005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:22.782517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:39.919000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:23.666765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:40.757352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:24.454858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:41.657560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:25.257945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:42.484700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:26.189619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:43.419049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:27.038787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:44.252661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:27.909143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:45.121078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:28.693802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:45.957124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:29.532505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:46.799985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:30.481139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:47.629112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:31.390318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:48.576738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:32.370922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:49.355253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:33.296469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:50.420915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:34.372555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:51.232276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:35.180693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:52.100647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:35.992781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.068239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:36.896092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.890074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:37.765091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:54.840696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:22.030309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:39.128062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:22.827236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:39.971314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:23.709350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:40.798556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:24.497210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:41.702533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:25.305009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:42.530625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:26.235775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:43.461207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:27.081391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:44.293515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:27.953879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:45.160726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:28.739316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:45.999800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:29.644115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:46.843968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:30.526037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:47.672458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:31.433701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:48.618451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:32.417973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:49.400030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:33.343561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:50.480129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:34.423872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:51.276909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:35.223785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:52.158568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:36.035910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.111996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:36.940409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.934039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:37.806510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:54.880886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:22.076192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:39.167471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:22.867652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:40.030419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:23.747610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:40.837692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:24.538092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:41.743058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:25.347839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:42.584358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:26.277642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:43.503997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:27.120893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:44.333714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:27.994001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:45.199472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:28.782038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:46.042432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:29.694001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:46.884334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:30.566986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:47.784482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:31.472970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:48.658798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:32.461408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:49.444152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:33.388859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:50.522472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:34.469418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:51.318859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:35.264293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:52.273864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:36.077749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.152762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:36.982466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.975634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:37.850016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:54.920948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:22.115840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:39.208054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:22.911006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:40.075777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:23.788124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:40.877871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:24.577235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

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2024-04-29T14:05:31.304504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:48.489326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:32.285389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:49.273299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:33.168413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:50.277155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:34.222204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:51.150651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:35.098059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:51.992380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:35.907753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:52.988471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:36.810780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:53.806100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF1

2024-04-29T14:05:37.633402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

DF2

2024-04-29T14:05:54.716332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

DF1

2024-04-29T14:05:38.691333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.

DF2

2024-04-29T14:05:55.596863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.

DF1

2024-04-29T14:05:38.856782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

DF2

2024-04-29T14:05:55.757622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DF1

credit_scoregeographygenderagetenurebalancenum_of_productshas_cr_cardis_active_memberestimated_salaryage_squaredbalance_sqrtlog_credit_scorecredit_score_num_of_productsage_balanceengagement_scorecustomer_value_normalizedproduct_densitybalance_salary_ratiocredit_score_age_ratiotenure_age_ratiocredit_salary_ratiobalance_indicatorlife_stagecs_categorytenure_groupcredit_score_genderbalance_agesalary_rank_geography
7759549GermanyFemale551137592.31201116548.023025370.9343746.30809810987567577.050.91.6599411.453570e-051.1805639.9818180.0181820.004711lowseniormediumnew652.63238180894.0012281019.0
7634501GermanyMale38988977.39201133403.071444298.2907816.21660610023381140.820.91.5402872.247762e-050.66698213.1842110.2368420.003756lowmiddle_agemediumlong_standing650.10000074039.6126761172.0
1337710GermanyFemale3010133537.10210155593.74900365.4272846.56526514204006113.000.71.8381661.497711e-050.85824223.6666670.3333330.004563lowadulthoodhighlong_standing652.63238174335.9979111342.0
7295648GermanyMale317125681.51101129980.93961354.5158816.4738916483896126.810.71.4271447.956620e-060.96692320.9032260.2258060.004985lowadulthoodmediumlong_standing650.10000077056.2607611140.0
2767598GermanyFemale2710171283.9111184136.12729413.8646036.3935915984624665.571.01.3892075.838260e-062.03579522.1481480.3703700.007108highadulthoodmediumlong_standing652.63238173370.241921731.0
2393850GermanyMale284147972.1911060708.72784384.6715356.7452368504143221.320.51.1742766.758027e-062.43741230.3571430.1428570.014001lowadulthoodhighintermediate650.10000072902.542280539.0
3843783SpainMale3810.0031180178.5414440.0000006.66313323490.001.41.1509083.000000e+060.00000020.6052630.0263160.009766lowmiddle_agehighnew650.10000074039.612676701.0
4921526SpainFemale338114634.63210110114.381089338.5773626.26530110523782942.790.71.5314681.744673e-051.04105015.9393940.2424240.004777lowadulthoodmediumlong_standing652.63238176651.244393981.0
8996615SpainMale325138521.8311156897.101024372.1852096.4216226154432698.561.01.1155747.219079e-062.43460319.2187500.1562500.010809lowadulthoodmediumintermediate650.10000077118.504224502.0
8948583FranceMale48391246.5311060017.462304302.0704066.3681875834379833.440.50.9328641.095932e-051.52033312.1458330.0625000.009714lowmiddle_agemediumnew650.10000082889.2444551015.0

DF2

credit_scoregeographygenderagetenurebalancenum_of_productshas_cr_cardis_active_memberestimated_salaryage_squaredbalance_sqrtlog_credit_scorecredit_score_num_of_productsage_balanceengagement_scorecustomer_value_normalizedproduct_densitybalance_salary_ratiocredit_score_age_ratiotenure_age_ratiocredit_salary_ratiobalance_indicatorlife_stagecs_categorytenure_groupcredit_score_genderbalance_agesalary_rank_geography
5702585FranceMale3670.0021094283.0912960.0000006.37161211700.000.70.9715822.000000e+060.00000016.2500000.1944440.006205lowmiddle_agemediumlong_standing652.52710383660.840098476.0
3667525GermanyMale334131023.7620055072.931089361.9720436.26339810504323784.080.41.2976811.526441e-052.37909615.9090910.1212120.009533lowadulthoodmediumintermediate652.52710374783.137912138.0
1617557SpainFemale4040.00201105433.5316000.0000006.32256511140.000.91.0273542.000000e+060.00000013.9250000.1000000.005283lowmiddle_agemediumintermediate646.30537682075.146316236.0
5673639SpainMale345139393.1920033950.081156373.3539746.45990412784739368.460.41.2253871.434790e-054.10582818.7941180.1470590.018822lowadulthoodmediumintermediate652.52710374842.03568270.0
4272640SpainFemale34377826.80111168544.851156278.9745516.4614686402646111.201.01.4032161.284904e-050.46175718.8235290.0882350.003797lowadulthoodmediumnew646.30537674842.035682390.0
8270559SpainMale3400.00110182988.9411560.0000006.3261495590.000.51.1652691.000000e+060.00000016.4411760.0000000.003055lowadulthoodmediumnew652.52710374842.035682429.0
7079595GermanyMale309130682.1121157862.88900361.4998066.38856111903920463.301.21.3102741.530431e-052.25847919.8333330.3000000.010283lowadulthoodmediumlong_standing652.52710360838.194412148.0
5295706GermanyFemale296185544.36110171037.63841430.7486046.5596157065380786.440.51.8450125.389547e-061.08481624.3448280.2068970.004128highadulthoodhighintermediate646.30537677276.269697421.0
845505FranceMale49780001.23100135180.112401282.8448876.2245585053920060.270.21.2449991.249981e-050.59181210.3061220.1428570.003736lowmiddle_agemediumlong_standing652.52710390635.463125693.0
5311714FranceFemale4000.0021062762.1216000.0000006.57088314280.000.70.8139222.000000e+060.00000017.8500000.0000000.011376lowmiddle_agehighnew646.30537682075.146316322.0

DF1

credit_scoregeographygenderagetenurebalancenum_of_productshas_cr_cardis_active_memberestimated_salaryage_squaredbalance_sqrtlog_credit_scorecredit_score_num_of_productsage_balanceengagement_scorecustomer_value_normalizedproduct_densitybalance_salary_ratiocredit_score_age_ratiotenure_age_ratiocredit_salary_ratiobalance_indicatorlife_stagecs_categorytenure_groupcredit_score_genderbalance_agesalary_rank_geography
1411685GermanyFemale30484958.60201194343.72900291.4765866.52941913702548758.000.91.8281442.354088e-050.43715622.8333330.1333330.003525lowadulthoodmediumintermediate652.63238174335.9979111699.0
6804692FranceFemale3070.0021118826.349000.0000006.53958613840.001.20.5941352.000000e+060.00000023.0666670.2333330.036757lowadulthoodmediumlong_standing652.63238174335.997911313.0
8048548SpainMale3360.0011131728.3510890.0000006.3062755480.001.00.4086481.000000e+060.00000016.6060610.1818180.017272lowadulthoodmediumintermediate650.10000076651.244393294.0
3615415FranceMale469134950.19300178587.362116367.3556726.02827912456207708.740.62.2090662.223042e-050.7556549.0217390.1956520.002324lowmiddle_agelowlong_standing650.10000069628.1323273140.0
7170678FranceMale360107379.6811184460.181296327.6883896.5191476783865668.481.01.1227589.312749e-061.27136518.8333330.0000000.008027lowmiddle_agemediumnew650.10000069000.6158311480.0
3239762SpainFemale1960.0021055500.173610.0000006.63594715240.000.70.7775112.000000e+060.00000040.1052630.3157890.013730lowadolescencehighintermediate652.63238162007.527647492.0
7088556FranceFemale544150005.38110157015.502916387.3052806.3207685568100290.520.51.6643576.666428e-060.95535410.2962960.0740740.003541highseniormediumintermediate652.63238181534.5594032762.0
5787729GermanyMale26497268.1021039356.38676311.8783426.59167414582528970.600.71.1048142.056173e-052.47147028.0384620.1538460.018523lowadulthoodhighintermediate650.10000078358.938043337.0
3456713FranceMale33694598.48100197519.661089307.5686596.5694817133121749.840.21.6344621.057099e-050.47893221.6060610.1818180.003610lowadulthoodhighintermediate650.10000076651.2443933460.0
1558571FranceFemale351104783.81201178512.521225323.7032756.34738911423667433.350.91.8321491.908692e-050.58698316.3142860.0285710.003199lowadulthoodmediumnew652.63238182639.8386943139.0

DF2

credit_scoregeographygenderagetenurebalancenum_of_productshas_cr_cardis_active_memberestimated_salaryage_squaredbalance_sqrtlog_credit_scorecredit_score_num_of_productsage_balanceengagement_scorecustomer_value_normalizedproduct_densitybalance_salary_ratiocredit_score_age_ratiotenure_age_ratiocredit_salary_ratiobalance_indicatorlife_stagecs_categorytenure_groupcredit_score_genderbalance_agesalary_rank_geography
4608850GermanyMale428119839.6910151016.021764346.1786976.7452368505033266.980.70.9828138.344481e-062.34906020.2380950.1904760.016661lowmiddle_agehighlong_standing652.52710374373.372424127.0
3641640SpainMale6230.00111101663.4738440.0000006.4614686400.001.00.7584971.000000e+060.00000010.3225810.0483870.006295lowseniormediumnew652.52710366343.824615222.0
966563FranceFemale346139810.34111152417.791156373.9122096.3332805634753551.561.01.5695987.152547e-060.91728416.5588240.1764710.003694lowadulthoodmediumintermediate646.30537674842.035682772.0
8538731SpainFemale335137388.01210165000.681089370.6588866.59441314624533804.330.71.8728811.455731e-050.83265122.1515150.1515150.004430lowadulthoodhighintermediate646.30537674783.137912382.0
5670497FranceMale3280.0021067364.4210240.0000006.2085909940.000.70.8369412.000000e+060.00000015.5312500.2500000.007378lowadulthoodlowlong_standing652.52710380080.022581351.0
5768674FranceMale362154525.7010127468.721296393.0975716.5132306745562925.200.71.0032836.471415e-065.62551518.7222220.0555560.024537highmiddle_agemediumnew652.52710383660.840098131.0
833634GermanyMale373111432.77211167032.491369333.8154736.45204912684123012.491.21.7795941.794804e-050.66713217.1351350.0810810.003796lowmiddle_agemediumnew652.52710375995.657174413.0
9719516GermanyFemale479128298.74100149614.172209358.1881356.2461075166030040.780.21.5096947.794309e-060.85753110.9787230.1914890.003449lowmiddle_agemediumlong_standing646.30537689368.100000372.0
2776689FranceMale3970.0020014917.0915210.0000006.53524113780.000.40.5746122.000000e+060.00000017.6666670.1794870.046189lowmiddle_agemediumlong_standing652.52710380612.25473081.0
3776750FranceMale332152302.7211071333.441089390.2598116.6200737505025989.760.51.2138246.565871e-062.13508222.7272730.0606060.010514highadulthoodhighnew652.52710374783.137912372.0

Duplicate rows

DF1

credit_scoregeographygenderagetenurebalancenum_of_productshas_cr_cardis_active_memberestimated_salaryage_squaredbalance_sqrtlog_credit_scorecredit_score_num_of_productsage_balanceengagement_scorecustomer_value_normalizedproduct_densitybalance_salary_ratiocredit_score_age_ratiotenure_age_ratiocredit_salary_ratiobalance_indicatorlife_stagecs_categorytenure_groupcredit_score_genderbalance_agesalary_rank_geography# duplicates
Dataset does not contain duplicate rows.

DF2

credit_scoregeographygenderagetenurebalancenum_of_productshas_cr_cardis_active_memberestimated_salaryage_squaredbalance_sqrtlog_credit_scorecredit_score_num_of_productsage_balanceengagement_scorecustomer_value_normalizedproduct_densitybalance_salary_ratiocredit_score_age_ratiotenure_age_ratiocredit_salary_ratiobalance_indicatorlife_stagecs_categorytenure_groupcredit_score_genderbalance_agesalary_rank_geography# duplicates
Dataset does not contain duplicate rows.